This document pertains generally to context-aware training and, particularly to training systems, apparatuses, and methods that select and provide training to a user based on action of a user.
Computer-Based Training systems and other forms of electronically supported learning and teaching (generically referred to as e-Learning systems) have traditionally relied on one-size-fits all training material, where the same collection of modules has to be taken by everyone. These modules may come in many different forms, including videos, flash-based presentations, simulations, training games and more. Independently of their format, they traditionally follow a fixed curriculum, where a predefined sequence of modules is prescribed for groups of individuals. Intelligent tutoring systems have introduced more sophisticated forms of computer-based training, where one develops and refines models of what the learner knows, and dynamically adapts learning content presented to the learner as these models evolve. When well designed, these systems have been shown to result in better outcomes than more traditional training modules.
Accordingly, it may be desirable to have a computer based training system that leverages sensed activity or behavior information in combination with user needs models that map those activities or behaviors onto quantitative or qualitative metrics indicating how critical it is for users engaging in these particular activities and behaviors to be knowledgeable of and proficient in different topics or training areas. Thus, embodiments of the present invention include computer-implemented systems and methods to selectively prioritize those areas where the learner needs to be trained and to selectively identify conditions where delivery of the training is likely to be most effective. That level of customization is thought to be particularly valuable in domains where training content is vast or opportunities for training are limited (e.g. limited time), and where the training required by individual users varies based on their activities and behaviors. Identifying training needs based on static information (e.g. based solely on the department an employee works for, or his/her level of education) is thought to be insufficient in these domains. Sensing activities, behaviors, or other contextual attributes can help better target training and mitigate consequences associated with undesirable behaviors.